1. Field of the Invention
This invention relates generally to comparison of cardiac waveform and, more particularly, to a system and a method for searching for similar ECGs to infer similar diseases by a matching of the shape of ECG time series.
2. Description of Background
An electrocardiogram (EKG or ECG) is an electrical recording of the heart that depicts the cardiac cycle. It is routinely used as a first course of choice in diagnosing many cardiovascular diseases. Often, twelve electrodes are used to record the electrical activity of the heart from different viewpoints. A normal ECG waveform (in lead II) has a characteristic shape as illustrated in FIG. 1A. The segment labeled P represents the phase of atrial depolarization/contraction when the impure blood enters the heart from the left atrium and pure blood enters the heart from the lungs into the right atrium (FIG. 1B). The QRS segment represents the phase of ventricular depolarization/contraction when blood enters the left and right ventricles for ejecting into the pulmonary and aorta respectively. Finally, the T segment represents ventricular repolarization where the ventricles relax to allow the cycle to begin again. Many disturbances in the heart function show as characteristic variations in the sinus rhythm waveform of FIG. 1A and can be used as cues to diagnose the disease. FIG. 1C shows such a modification in the ECG due to premature ventricular contraction where the heart skips a beat only to beat very strongly in the next causing a missed R segment. Physicians routinely make diagnosis by a simple visual examination of these ECG waveforms. It is common knowledge to physicians that patients with the same disease have similar-looking ECG shape in the relevant channels (leads). Examples of such similarity can be seen in FIG. 2, which shows ECG recording of several patients all diagnosed with bundle branch block.
This observation of similarity, however, is after factoring out a number of morphological variations that can be attributed to heart rate variability, disease-specific variability, and measurement variability in ECG recordings that affect the amplitude levels. Further, there seems to be a built-in tolerance to disease-specific variability that often manifests as small relative translation of characteristic segments of the ECG such as the P, Q, R, S, and T while still preserving the shape of the segments.
There are a number of algorithms available for single ECG analysis, and for ECG classification based on neural network, expert and fuzzy expert systems, machine learning methods, wavelet transforms and genetic algorithms. The rule-based methods rely on the accuracy of the P-Q-R-S-T segment detection. Errors in estimation of these feature values can cause major errors in disease-specific interpretation. Further, in order to distinguish combinations of diseases, a finer shape analysis of the ECG waveform may be required. The parametric modeling methods, on the other hand, are good at spotting major disease differences but can't take into account fine morphological variability due to heart rate (e.g., ventricular vs. supra-ventricular tachycardia) and physiological differences.
Related work in the time alignment of ECGs also exists. Dynamic time warping (DTW) has been a popular technique in ECG frame classification, and more recently, in the recognition of heart beat patterns for synthetically generated signals. In all such alignments, however, the amplitude of the signal was used rather than a detailed modeling of the shape. Moreover, the DTW algorithm used did not explicitly model the morphological changes in the signal across patients with similar diseases, as it does not take into account missing and spurious fiducial features during alignment.